Superficial venous thrombosis of the upper limb presenting to a chiropractic clinic: a case report.
Bibliographic record
Abstract
OBJECTIVE: To present the clinical case of a patient with an upper extremity superficial venous thrombosis (SVT), and to highlight the importance for clinicians working in musculoskeletal care settings, to considered non-musculoskeletal causes for their patients' presentations. CLINICAL FEATURES: A 31-year-old male presented to an academic chiropractic clinic with progressive left sided tension over the medial arm, extending to the anterior aspect of his proximal forearm. INTERVENTION AND OUTCOME: The patient was initially diagnosed with possible biceps/brachialis muscular strain and peripheral entrapment of the median nerve. A course of treatment involving soft tissue therapy was initiated. Unfortunately, the patient's symptoms worsened, and on further evaluation, near full occlusion and phlebitis of the left cephalic vein was discovered. Symptoms dissipated over the next few days with conservative medicinal efforts. SUMMARY: Although not often viewed as a serious condition, or factored in the differential diagnoses of musculoskeletal practitioners, to not consider SVT as a cause of a patient's symptoms may lead to a protracted clinical course and increased discomfort for the patient, and in rare cases, more serious consequences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".